1. Field of the Invention
Embodiments of the invention generally relate to an integrated processing system containing multiple processing stations and robots that are capable of processing multiple substrates in parallel.
2. Description of the Related Art
The process of forming electronic devices is commonly done in a multi-chamber processing system (e.g., a cluster tool) that has the capability to sequentially process substrates, (e.g., semiconductor wafers) in a controlled processing environment. Typical cluster tools used to deposit (i.e., coat) and develop a photoresist material, commonly known as a track lithography tool, or used to perform semiconductor cleaning processes, commonly described as a wet/clean tool, will include a mainframe that houses at least one substrate transfer robot which transports substrates between a pod/cassette mounting device and multiple processing chambers that are connected to the mainframe. Cluster tools are often used so that substrates can be processed in a repeatable way in a controlled processing environment. A controlled processing environment has many benefits which include minimizing contamination of the substrate surfaces during transfer and during completion of the various substrate processing steps. Processing in a controlled environment thus reduces the number of generated defects and improves device yield.
The effectiveness of a substrate fabrication process is often measured by two related and important factors, which are device yield and the cost of ownership (CoO). These factors are important since they directly affect the cost to produce an electronic device and thus a device manufacturer's competitiveness in the market place. The CoO, while affected by a number of factors, is greatly affected by the system and chamber throughput, or simply the number of substrates per hour processed using a desired processing sequence. A process sequence is generally defined as the sequence of device fabrication steps, or process recipe steps, completed in one or more processing chambers in the cluster tool. A process sequence may generally contain various substrate (or wafer) electronic device fabrication processing steps. In an effort to reduce CoO, electronic device manufacturers often spend a large amount of time trying to optimize the process sequence and chamber processing time to achieve the greatest substrate throughput possible given the cluster tool architecture limitations and the chamber processing times.
In track lithography type cluster tools, since the chamber processing times tend to be rather short, (e.g., about a minute to complete the process) and the number of processing steps required to complete a typical process sequence is large, a significant portion of the time it takes to complete the processing sequence is taken up transferring the substrates between the various processing chambers. A typical track lithography process sequence will generally include the following steps: depositing one or more uniform photoresist (or resist) layers on the surface of a substrate, then transferring the substrate out of the cluster tool to a separate stepper or scanner tool to pattern the substrate surface by exposing the photoresist layer to a photoresist modifying electromagnetic radiation, and then developing the patterned photoresist layer. If the substrate throughput in a cluster tool is not robot limited, the longest process recipe step will generally limit the throughput of the processing sequence. This is usually not the case in track lithography process sequences, due to the short processing times and large number of processing steps. Typical system throughput for the conventional fabrication processes, such as a track lithography tool running a typical process, will generally be between 100-120 substrates per hour.
Other important factors in the CoO calculation are the system reliability and system uptime. These factors are very important to a cluster tool's profitability and/or usefulness, since the longer the system is unable to process substrates the more money is lost by the user due to the lost opportunity to process substrates in the cluster tool. Therefore, cluster tool users and manufacturers spend a large amount of time trying to develop reliable processes, reliable hardware and reliable systems that have increased uptime.
FIG. 1 illustrates a top view of a conventional cluster tool configuration (e.g., cluster tool 22) that contains three processing cells 16 that each contain a robot 17, one or more pass-through slots 21 (e.g., robots 21A-21C), and one or more processing chambers 18 that surround each of the robots 17 (e.g., robots 17A-17C). The cluster tool 22 will also generally contain a front end unit 19 that contains a front end robot 20 and one or more substrate cassettes 23. In these type of conventional cluster tool configurations the size and number of processing chambers 18 are limited by the reach of the robots 17A-C and thus can not be increased in size unless another processing cell (e.g., item # 16′) is added to the cluster tool. When a new processing cell 16′ is added, a new robot has to be added to the cluster tool 22 so that substrates can be transferred to the added processing chambers 18. The process of transferring substrates through the cluster tool 22 needs to be done “serially”, rather than in parallel, since each substrate must be transferred from one processing cell 16 to another by use of the robot positioned in the center of each cell. An issue arises since the reliability of a serial sequence is proportional to the product of the reliability of each component in the sequence. Therefore, by adding additional robots to the cluster tool the reliability of the system will drop. For example, a transferring sequence that uses two robots that have an up-time of 99% each, will limit the system's uptime to 98.01%, whereas a system that was able to utilize a single robot to service the same number of chambers would have an uptime of 99%. Therefore, since system uptime is a major factor in CoO calculations there is a need for a cluster tool that minimizes the number of serial steps and serial components.
Prior art configurations, such as the one shown in FIG. 1, require the use of multiple pass-through slots 21 distributed throughout the cluster tool 22, and multiple robots to complete the transferring process sequence through the cluster tool. For example, a first robot 17A will handoff each substrate to a pass-through slot 21B so that the adjacent second robot 17B can pickup and transfer the substrate to a desired position within a processing chamber in its processing cell 16. After the substrate is processed in the processing chamber the substrate is then placed back in the pass-through slot 21B by the second robot 17B where it is picked up by the first robot 17A. Conventional cluster tool transferring sequences that require multiple handoffs to pass-through chambers are detrimental to CoO calculations, since it requires a number of non-value added moves to transfer the substrate between various processing cells 16 within the cluster tool 21. The non-value added moves can be costly due to decreased substrate throughput and the decrease in the cluster tool reliability. Since track lithography chamber processing times tend to be rather short, and the number of processing steps required to complete a typical process sequence is large, the system throughput can be significantly affected by the number of wafer handoffs, the non-value added moves of a robot, and the reliability of the components within the system. Therefore, there is a need for a cluster tool that reduces the number of non-value added moves, such as pass-through steps.
Another issue that arises when building a cluster tool that have a large number of processing chambers and supporting components, which are common to lithography type cluster tools, is that the cluster tool is hard to manufacture, the cluster tool is not easily serviced during operation, and the cluster tool is not easily configured to meet the end user's needs. These issues commonly arise due to the competing goals that require the footprint of the cluster tool to be as small as possible versus the cluster tool having enough chambers and robotic components to assure that the throughput of the system achieves a desired goal. Therefore, there is a need for a cluster tool that is easy to manufacture, is easy to service, is easily configured, and has a small footprint relative to the prior art type configurations.
The push in the industry to shrink the size of semiconductor devices to improve device processing speed and reduce the generation of heat by the device, has reduced the industry's tolerance for process variability. To minimize process variability an important factor in the track lithography processing sequences is the issue of assuring that every substrate run through a cluster tool has the same “wafer history.” A substrate's wafer history is generally monitored and controlled by process engineers to assure that all of the device fabrication processing variables that may later affect a device's performance are controlled, so that all substrates in the same batch are always processed the same way. To assure that each substrate has the same “wafer history” requires that each substrate experiences the same repeatable substrate processing steps (e.g., consistent coating process, consistent hard bake process, consistent chill process, etc.) and the timing between the various processing steps is the same for each substrate. Lithography type device fabrication processes can be especially sensitive to variations in process recipe variables and the timing between the recipe steps, which directly affects process variability and ultimately device performance. Therefore, a cluster tool and supporting apparatus capable of performing a process sequence that minimizes process variability and the variability in the timing between process steps is needed. Also, a cluster tool and supporting apparatus that is capable of performing a device fabrication process that delivers a uniform and repeatable process result, while achieving a desired substrate throughput is also needed.
Therefore, there is a need for a system, a method and an apparatus that can process a substrate so that it can meet the required device performance goals and increase the system throughput and thus reduce the process sequence CoO.